JP4619781B2 - System for detecting plaque components - Google Patents

Description

  The present invention relates to imaging systems, and more particularly to methods and systems for detecting plaque components.

  Recent advances in computed tomography (CT) technology have achieved faster scan speeds, greater coverage using multiple detector rows, and thinner slices. However, the energy resolution of measurements obtained using these systems is still unachieved. The wide X-ray photon energy spectrum output from the X-ray source and the lack of energy resolution due to the CT detection system hinder energy discriminating CT.

The attenuation of X-rays that have passed through a given subject is highly dependent on the incident X-ray photon energy. This physical phenomenon manifests itself in the image as beam hardening artifacts such as inhomogeneities, shadows and streaks. Some beam hardening artifacts can be easily corrected, while other beam hardening artifacts may be more difficult to correct. In general, well-known methods for correcting beam hardening artifacts include water calibration methods that include calibrating each CT device to eliminate beam hardening due to materials similar to water, as well as first pass images. An iterative bone correction method is included in which the bone is separated at the reconstruction and then these measurements are corrected for the beam hardening due to the bone at the second pass reconstruction. However, beam hardening with materials other than water and bone (eg, metals and contrast agents) can be difficult to correct without additional processing on the measured data. Furthermore, even if the correction method described above is used, the conventional CT cannot provide a quantitative image value. On the contrary, the same material at various locations in the image is often displayed with different CT values.
Cheng Hong, Kyongtae T. Bae and Thomas K.M. Pigram, “Coronary Arterial Calcium: Accuracy and Reproducibility of Measured by Measured by the Seventh of the Seventh of the Multi-Detector of the Third Sandra S. Halliburton, Arthur E. et al. Stillman, Richard D. White, “Nonvasive quantification of coronary articulation: Methods and prognostic value” (Cleveland Clinic Journal of Medicine, Volume 69, Volume 69) Fayad et al., “CT and MR for coronary angiography & plaque imaging” (Circulation, October 2002, pages 2026-2033).

  Another drawback of conventional CT is the reduced level of material characterization. For example, a material with high attenuation at low density and a material with lower attenuation at high density may have the same CT value in the image. Therefore, based on the CT value alone, little or no information regarding the material composition of the scanned specimen can be obtained.

  In one aspect, a method is provided. The method includes detecting a plaque component using a multiple energy computed tomography (MECT) system.

  In another aspect, a method for detecting plaque components is provided. The method includes creating information about phantom projection data using the MECT system and obtaining plaque components from this information.

  In yet another aspect, a MECT system is provided. The MECT system includes at least one radiation source configured to emit X-rays that intersect the subject, at least one detector configured to detect the X-rays, and the detector. And a computer configured to instruct the MECT system to detect plaque components.

  In yet another aspect, a computer readable medium encoded with a program is provided. The program is configured to instruct the computer to detect a plaque component in a subject that is scanned using a multiple energy tomography (MECT) system.

  In yet another aspect, a computer encoded with a program is provided. The program is configured to instruct the MECT system to detect plaque components in a subject that is scanned using the MECT system.

  As arteriosclerosis progresses, deposits containing fat are deposited on the blood vessel wall, which is thought to cause progressive stenosis in blood vessels such as the carotid artery and coronary artery. Ultimately, tissue such as the myocardium and brain tissue is deprived of oxygen-carrying blood and the blood vessel lumen or blood flow is reduced to such a level that the tissue dies, which can lead to heart attacks and strokes. Become. In contrast, in atherosclerosis, the majority of patients with acute heart disease have an inflammatory process within the vessel wall itself. Low density lipoprotein (LDL) accumulates in the vessel wall and LDL further draws immune system cells into the vessel wall. The immune system cells take in the deformed LDL and produce lipid droplets that make up the lipid nuclei of the plaque. Inflammatory cells promote plaque growth and the development of a fibrous cap composed of collagen fibers formed by smooth muscle cells to cover the lipid nucleus. When inflammatory cells are expressed, immune system cells degrade collagen and secrete enzymes that prevent the development of new collagen fibers for the repair of cap damage. This weakening of the cap may continue until cracks occur and the luminal blood mixes with protein-rich lipid nuclei that promote blood clotting. This can form a blood clot and block the blood vessel, resulting in an ischemic condition. If the clot is not occlusive, scar tissue develops and the plaque expands, thereby leading to a chronic condition.

  In coronary atherosclerosis, there may be blood clots, as well as calcified plaques, intermediate plaques, and soft plaques. Plaque composition is thought to be an indicator of the risk of acute coronary syndrome. Soft plaques contain high lipid concentrations, thin fibrous caps, and inflammatory cells. Intermediate plaques contain fibrous tissue, lower concentrations of lipids, and inflammatory cells. Calcified plaque contains a high concentration of calcium. The presence of soft plaque can increase the risk of stroke and heart attack. The thrombus is believed to have a density of less than 20 Hounsfield values (HU). Soft plaques have a density of less than 50 HU, intermediate plaques have a density between 50 and 120 HU, and calcified plaques have a density of more than 120 HU. The Hounsfield value, also known as the CT value, is used to control the brightness of the corresponding pixel on the cathode ray tube display and reflect the consistency of the various types of plaques. However, it is difficult to identify and identify plaques that have low or no calcium.

  In some configurations of CT imaging systems, the x-ray source emits a fan-shaped beam that is collimated to lie in the XY plane, commonly referred to as the “imaging plane” of the Cartesian coordinate system. The X-ray beam passes through an imaging target such as a patient. After the beam is attenuated by this object, it is incident on the array of radiation detectors. The intensity of the attenuated radiation beam received at the detector array depends on the attenuation of the x-ray beam by the object. Each detector element of the array separately generates an electrical signal corresponding to a measurement of beam attenuation at the respective detector location. The attenuation measurements from all detectors are collected separately and a transmission profile is created.

  In the third generation CT system, the X-ray source and the detector array rotate with the gantry around the imaging target in the imaging plane so that the angle at which the X-ray beam cuts the imaging target changes constantly. A group of processed X-ray attenuation measurements (ie, projection data) obtained from a detector array at a gantry angle is referred to as a “view”. Further, “scan data (scan)” to be imaged consists of a set of views obtained at various gantry angles (that is, view angles) while the X-ray source and the detector rotate once.

  In the axial scan, this projection data is processed, and an image corresponding to a two-dimensional (2D) slice obtained by cutting out the imaging target is constructed. One method for reconstructing an image from a set of projection data is what is referred to in the art as the filtered back projection technique. In this processing method, attenuation measurement values obtained by scanning are converted into integers called Hounsfield values.

  In order to reduce the overall scan time, a “helical” scan may be performed. To perform a “helical” scan, the subject is moved within the scanner while collecting data for a predetermined number of slices. In such a system, a single helical scan of the X-ray fan beam yields a single helix. Projection data is obtained by a spiral drawn by an X-ray fan beam, and this can be used to reconstruct an image at each predetermined slice position.

  The reconstruction algorithm for helical scans typically uses a helical weighting algorithm that weights the collected data as a function of view angle and detector channel index. Specifically, prior to the filtered back projection process, the data is weighted according to a helical weighting factor that is a function of both the gantry angle and the detector angle. The helical weighting algorithm further scales this data according to a scale factor that is a function of the distance between the x-ray source and the subject. Next, the weighted and scaled data is processed to generate a CT value, and an image corresponding to a 2D slice obtained by cutting the subject is formed.

  As used herein, an element or step prefixed with the words “a” or “an” in the singular does not exclude a plurality of elements or steps in this regard (an explicit exclusion of such an exclusion). Should be understood). Furthermore, references to “one embodiment” of the present invention are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features.

  Further, as used herein, the phrase “reconstruct an image” is intended to exclude embodiments of the present invention that produce data representing the image but not an observable image. It was n’t. However, many embodiments create (or are configured to create) at least one observable image.

  Referring to FIGS. 1 and 2, a multi-energy multi-slice scanning imaging system (eg, multi-energy computed tomography (MECT) imaging system 10) is used in a gantry 12 typical of “third generation” CT imaging systems. It is expressed as including. The method for detecting plaque components is implemented using the MECT system 10. The gantry 12 has an x-ray source 14 that projects an x-ray beam 16 toward a detector array 18 on the opposite surface of the gantry 12. The detector array 18 is formed by a plurality of detector rows (not shown) including a plurality of detector elements 20 that integrally detect projection X-rays that have passed through a subject such as a patient 22. . Each detector element 20 generates an electrical signal representative of the intensity of the incident x-ray beam and can therefore be used to estimate the beam attenuation as it passes through the patient 22. During a scan to collect x-ray projection data, each component mounted on the circular portion of the gantry 12 rotates about a center of rotation 24 within the gantry. FIG. 2 represents only a single row of detector elements 20 (ie, one detector row). However, the multi-slice detector array 18 includes a plurality of parallel detector rows of detector elements 20 that can simultaneously collect projection data corresponding to a plurality of pseudo-parallel slices or parallel slices during a single scan. Further, the detector array 18 may be an area detector that facilitates a large imaging coverage on the patient 22 for each rotation of the circular portion of the gantry 12.

  The rotation of each component in the gantry 12 and the operation of the X-ray source 14 are controlled by the control mechanism 26 of the MECT system 10. The control mechanism 26 includes an X-ray controller 28 that supplies power and timing signals to the X-ray source 14, and a gantry motor controller 30 that controls the rotational speed and position of each component in the gantry 12. Within the control mechanism 26 is a data acquisition system (DAS) 32 that samples analog data from the detector elements 20 and converts this data into digital signals for subsequent processing. The image reconstruction device 34 receives the sampled and digitized X-ray data from the DAS 32 and performs image reconstruction at high speed. The reconstructed image is provided as an input to the computer 36 and stored in the storage device 38 by the computer. The image reconstruction device 34 can be specialized hardware or a computer program executed on the computer 36.

  The computer 36 further receives commands and scanning parameters from an operator via a console 40 having a keyboard. The attached cathode ray tube display 42 allows the operator to observe the reconstructed image and other data from the computer 36. The computer 36 provides control signals and control information to the DAS 32, the X-ray control device 28, and the gantry motor control device 30 using commands and parameters issued by the operator. In addition, the computer 36 operates a table motor controller 44 for controlling the motor driven table 46 to position the patient 22 within the gantry 12. Specifically, the table 46 allows each part of the patient 22 to pass through the gantry opening 48.

  In one embodiment, the computer 36 is a device 50 (eg, a flexible disk drive or a CD-ROM drive) for reading instructions and / or data from a computer readable medium 52 such as a flexible disk or CD-ROM. )including. In another embodiment, computer 36 executes instructions stored in firmware (not shown). Computer 36 is programmed to perform the functions described herein, and as used herein, the term computer is not limited to merely an integrated circuit referred to in the art as a computer. , Controller, processor, microcontroller, microcomputer, programmable logic controller, application specific integrated circuit, and other programmable circuits, and these terms are not distinguished in this specification. I use it. The MECT system 10 is one of the energy discriminating computed tomography (EDCT) systems in that it is configured to respond to various incident x-ray spectra. This system can be achieved using a conventional third generation CT system by collecting projection data while sequentially using various x-ray tube voltages. For example, two scans are collected either continuously or alternately so that the X-ray tube operates at a potential of 80 kVp and 160 kVp, which generate low energy and high energy spectra, respectively. Alternatively, special filters are placed between the x-ray source 14 and the detector array 18 so that the various detector rows collect different x-ray energy spectrum projections. In yet another embodiment, an energy sensitive detector will be used so that each X-ray photon reaching the detector is recorded with its photon energy. Although the specific embodiments referred to above relate to third generation CT systems, the methods described herein can be applied to fourth generation CT systems (stationary detector-rotating x-ray source) or second generation CT systems. It applies equally to five generation CT systems (stationary detectors and x-ray sources), as well as other generation CT systems of higher generations.

  EDCT can reduce or eliminate problems generally associated with some CT systems, such as lack of energy identification and material characterization. Since there is no subject scattering, the MECT system 10 can be used to separately detect two regions of the incident photon energy spectrum, a low energy portion and a high energy portion of the incident X-ray spectrum. The behavior at any other energy can be derived based on signals from these two energy regions. This phenomenon is based on the fundamental fact that in the energy region where medical CT is concerned, two physical processes are dominant in terms of X-ray attenuation: (1) Compton scattering and (2) photoelectric effect. It depends. In order to characterize the behavior of the subject causing the attenuation of the X-ray beam, two independent parameters are measured. Thus, the detection signals from the two energy regions provide sufficient information to resolve the energy dependence of the object being imaged, and thus the material composition can be characterized.

  Data analysis used in EDCT includes Compton and photoelectric decomposition and / or substrate decomposition (BMD). In Compton and photoelectric decomposition, instead of acquiring a single image that characterizes the overall attenuation coefficient in the reconstructed CT image, a pair of images showing attenuation due to the Compton process and photoelectric process using MECT 10 are obtained. Created. Further, by slightly modifying this process, it is possible to create an image representing the density and effective atomic number. The BMD method is based on the idea that the X-ray attenuation of any given material in the energy range for MECT can be expressed by assuming a density mixture of two other known materials. These two materials are referred to as a basis material. Using BMD, two reconstructed images are obtained such that each image represents the equivalent density of one of the substrates. Because density is independent of x-ray photon energy, these images are relatively free of beam hardening artifacts. Furthermore, since these substrates are selected with the material of interest as the target, the image contrast is improved.

  To optimize a multi-energy CT system that has not realized energy discrimination using photon counting, it should be noted that the greater the energy separation in the X-ray spectrum, the better the image quality. Furthermore, the photon statistics in these two energy regions should be equivalent. Otherwise, noise is dominant in the reconstructed image in the energy region with less statistical information.

  To obtain a dual energy measurement, (1) for a scan with two different energy spectra, (2) for the detection of photon energy according to the penetration depth at the detector position, or (3) There are various methods for photon counting using energy discrimination. Photon counting provides a clear spectral separation as well as an adjustable energy separation threshold for balancing photon statistics.

  3, 4, 5 and 6 represent a flow diagram of one embodiment relating to a method for detecting a component of a plaque, such as an atherosclerotic plaque. This method is executed by the computer 36 when an image reconstructed by the image reconstruction device 34 is received. The method includes a step 66 of detecting plaque components such as calcium, lipids and smooth muscle cells developed in the patient 22 and a step 68 of quantifying the plaque components. In order to detect these components, the MECT system 10 is used. These components are detected by creating information about the density of the plaque components and characterizing the plaque components with this information. This information can be created to allow the MECT system 10 to create a reconstructed image of the phantom by simulating data collection on projection data obtained using the subject (eg, phantom). . For phantoms, we assume a material decomposition base such as water and iodine. This phantom includes regions with known material properties and known densities of water and iodine. For example, a phantom includes a region with a known concentration of iodine solution and thus a region with a known water density and iodine density. In another example, the phantom includes a region with another known concentration of iodine solution.

  To create the information, the first and second sets of projection data are created by extending the entire space of iodine and water concentrations of interest into the phantom. The first set of projection data includes a step 70 of simulating the placement of the phantom within the scan range of the MECT system 10 and a step 72 of scanning the phantom with the first energy spectrum using the MECT system 10. Has been created by. As an example, the first energy spectrum may be a high energy spectrum or a low energy spectrum. Thus, for a known water density and iodine density, projection data for a known material is obtained corresponding to the first energy spectrum. The second set of projection data for the phantom is the first set of projection data, except that the second set of projection data is created by simulating a phantom scan using the second energy spectrum. (74). The second energy spectrum is a low energy spectrum if the first energy spectrum is a high energy spectrum. Conversely, the second energy spectrum is a high energy spectrum if the first energy spectrum is a low energy spectrum. After the simulation of data collection for the first and second energy spectrum scans is complete, a specific material-based decomposition lookup table is formed for all known various iodine concentrations (76). . When using iodine concentration, this lookup table maps the known projection data created from the water and iodine measurements in the phantom so that the first set of images is the first set of projection data. , And a second set of images is created from the second set of projection data, enabling the creation of density distributions in the first and second sets of images. After building the look-up table, a step 78 of scanning an object, such as a patient 22, for unknown material to create projection data, and using the look-up table to create a density image of the substrate In step 80, plaque components are obtained. As an example, the lookup table is inverse mapped so that the material distribution in the patient 22 is derived from the distribution of material density in the phantom.

  A detailed description of the process that enables the creation of a density image of a substrate using a look-up table is given in July 2003 with reference number 127076-2, which is hereby incorporated by reference in its entirety. It is described in an application entitled “Method and Apparatus For Generating a Density Map Using Dual-Energy CT” filed on the day. A detailed description of the process that enables the creation of a density image of a substrate using a look-up table has application number 60 / 397,658, which is also hereby incorporated by reference in its entirety. It is described in the provisional application filed on July 23, 2002.

  Plaque components are used to place the patient 22 within the scan range of the MECT system 10 and to scan the patient 22 with the first and second energy spectra using the MECT system 10 to restore the density of the substrate within the patient 22. Obtained by getting the configuration. In another embodiment, the method includes scanning a first small section of patient 22. This first small cross section contains cells or elements that are present in the lipid nuclei of the plaque but not in the smooth muscle of the plaque. In yet another alternative embodiment, the method includes scanning a second minor section of the patient 22. The second small section contains plaque. Note that additional energy levels can be used to scan a phantom or subject to create a lookup table.

  The method further includes a step 68 of quantifying the components of the plaque. The method quantifies plaque components by calculating a composition density distribution, such as lipid loading or calcium loading, for the plaque and further calculating the total plaque load. As an example, this total plaque load is calculated by a threshold method. The threshold method distinguishes pixels having a density greater than a certain number y. These pixels are pixels of an image acquired by using the MECT system 10. The total plaque load within an organ of the patient 22 is a weighted sum of densities over y for pixels of the organ's image. The composition density distribution is calculated by a method similar to that used to calculate the total plaque load. For example, lipid loading has been calculated by distinguishing pixels that have a density above a certain number v for images obtained using the MECT system 10. The total lipid load in an organ of the patient 22 is a weighted sum of density exceeding v for pixels of the organ image. In another embodiment, the amount of each component is combined with a measurement of the lumen geometry of patient 22 to assess the degree and severity of stenosis. Another technique to quantify the components of the plaque, "Coronary Artery Calcium: Accuracy and Reproducibility of Measurements with Multi-Detector Row CT-Assessment of Effects of Different Thresholds and Quantification Methods 1" (Cheng Hong, Kyongtae T.Bae And Thomas K. Pilgram, Radiology 2003, 227: 795-801), “Nonvasive quantitative of qualification: Methods and pr. gnostic value "(Sandra S.Halliburton, Arthur E.Stillman, Richard D.White, Cleveland Clinic Journal Of Medicine Volume 69, Supplement), as well as" CT and MR for coronary Angiography & plaque imaging "(Fayad et al., Circulation, 2002 year October, pages 2026-2033).

  The method further includes the step 104 of instructing the MECT system 10 to perform additional scans on the patient 22 at various times and repeating step 66 shown in FIG. 3 for each scan performed. Including. The method includes a step 106 of waiting for a user to administer a contrast agent, such as a non-ionic iodine, gadolinium, blood pool contrast agent and / or contrast agent attached to a specific molecule. This contrast agent is administered to enhance the contrast of at least one of a lipid-avid agent, plaque specific antigen and / or plaque cells of patient 22. The method further includes the step 107 of repeating the step 66.

  The method further includes waiting 108 for the user to administer a temperature sensitive contrast agent to highlight the inflammatory plaque. Examples of temperature sensitive contrast agents include agents containing lanthanide metals such as Ce, Pr, Nd, Sm, Eu, Gd, Db, Dy, Ho, Er, Tm and Yb. The method includes step 109 of repeating step 66.

  As shown in FIG. 5, the method further includes at least one of a two-dimensional (2D) image and a three-dimensional (3D) image relating to a plaque component on the wall of an organ such as the intestinal tract or blood vessel of the patient 22. Displaying 110 and allowing viewing from a viewing point for a volume of plaque in a 3D image. The method performs volume rendering to observe a 3D image or to use a rendering method such as changing the orientation from at least one viewing point in the 3D image. Volume rendering is one technique used to render a sampled function in spatial three dimensions. Volume rendering is applied to medical imaging where volume data is available by the MECT system 10. The image reconstruction device 34 creates a three-dimensionally stacked image (that is, slice) of parallel planes, each of which is composed of an array of material densities. Some CT images have a resolution of 512 x 512 x 12 bits and contain up to 500 slices stacked. In the 2D region, it is possible to observe slices by one slice at a time. One advantage of CT images obtained from the image reconstructor 34 is that each slice contains information from only one plane. The usefulness of stacking parallel data generated by the MECT system 10 has facilitated the development of techniques for observing volume data as a three-dimensional field rather than as individual slices. Therefore, the volume data can be observed from an arbitrary observation point here.

  The method includes a step 111 of repeating step 66 after scanning a patient with a metal stent and / or metal valve. Beam hardening artifacts are removed by step 111 repeating step 66. Since the MECT system 10 has been scanned with multiple energies as described above to obtain an image of the material density in the patient 22 including the stent, beam curing artifacts are removed by step 111 repeating step 66. Further, step 111, repeating step 66, allows for the detection of restenosis in the stent, which is the reoccurrence of in-stent plaques, by improving the quality of the image obtained using the MECT system 10. It should be noted that the actual placement and actual scan for the phantom is performed using the MECT system 10, not the simulation of the phantom scan in step 70. In addition, by scanning a phantom with different compositions (eg calcium, iodine and water) with additional energies such as third and fourth energies, an additional list of materials and energies can be obtained. Note also that it is created. This additional list is added to the information in the lookup table.

  Note that the steps of the flowcharts of FIGS. 3, 4, 5 and 6 can be performed in a different order than shown. For example, steps 108 and 109 can be performed before step 106 and after step 104. In another example, steps 106 and 107 may be performed before step 110 and after step 109. Furthermore, it is not always necessary to execute all the procedures shown in FIGS. 5 and 6 in order to obtain useful information.

  FIG. 7 shows a plurality of images including the image 130, the image 132, and the image 134. Image 130 is an image reconstructed using conventional CT imaging techniques. The dense organ of image 130 includes calcium 136 and iodine 138 at a density that is reconstructed to the same effective CT value (ie, Hounsfield value). Accordingly, there is no visible difference between calcium 136 and iodine 138 in the image 130. Image 132 is an iodine density image using iodine 138 as a substrate, and image 134 is a water density image using water as a substrate. Images 132 and 134 are created by implementing the methods of FIGS. Specifically, the images 132 and 134 are acquired by realizing the process 66 including the substrate decomposition process of the present method. This method enables discrimination between calcium 136 visible as a white area and iodine 138 visible as a gray area in the image 132. Since calcium 136 and iodine 138 each have different attenuation characteristics that are a function of the x-ray spectral energy, they can be distinguished from each other. Calcium 136 is clear in both images 132 and 134 because it is not the substrate used for degradation.

  FIG. 8 shows a plurality of images including an image 150, an image 152, and an image 154. Image 150 is an image reconstructed using conventional CT imaging techniques. The dense organs in image 150 exhibit beam hardening artifacts such as inhomogeneities, shadows and streaking as occurs in some CT images. An image 152 is an iodine density image using iodine 138 as a substrate, and an image 154 is a water density image using water as a substrate. Images 152 and 154 were created by implementing the methods of FIGS. Specifically, the images 152 and 154 are obtained by implementing the substrate decomposition method in combination with the step 66. In images 152 and 154, non-uniformity, shadows and streaking are removed. Combining the substrate decomposition method with the above implementation of step 66 results in images 152 and 154 that are free of beam hardening artifacts.

  Thus, the systems and methods for detecting plaque components described herein take into account system behavior, such as detector response and detector signal accumulation. These systems and methods showed improved results as represented in FIGS. Possible applications of these systems and methods include coronary imaging and calcium scoring using protocols that specify contrast agent injection. Other possible applications include improving in-stent image quality by eliminating stent beam hardening artifacts, enabling assessment of in-stent restenosis, and plaque characterization. In addition, these systems and methods allow for continuous screening against high-risk patient populations that can be selected based on genetic or environmental medical history factors. These systems and methods further allow for automatic detection of high-risk vascular regions that represent unstable plaque regions, based on vessel geometry that provides tremendous benefits to the physician. These systems and methods allow for the automatic detection of plaque load and composition that also benefits the physician. This automatic detection allows for a more complete detection of disease (especially the initial disease marker) within a large projection data set and allows automatic quantification of disease at all vascular sites. Automatic calculations may be utilized to fully detect each plaque area and characterize it in stages corresponding to different risk groups or intervention / treatment groups. The calculated value may then be highlighted during the doctor's interpretation, and a follow-up interval or scan protocol may be recommended. Utilizing several methods, including advanced pattern recognition methods such as neural networks and model-based algorithms, to automatically detect and diagnose CVD after detecting cardiovascular disease (CVD) it can. Further, when the MECT system 10 performs a plurality of scans at various points in time, it is possible to detect the progress and regression of CVD. The degree of progression of the CVD can be measured within the patient 22 and compared to the normal population taking into account age, gender and other bias factors. These comparisons help CVD identify high-risk patients 22 and allow treatment monitoring. The advanced pattern recognition method is again for data collected using the MECT system 10 as well as for other patient data to ensure that the patient 22 is optimally treated. Can be used.

  The systems and methods described herein may be used as image-based surrogates for drug treatment clinical trials. This allows assessment of drug effectiveness during initial drug development and clinical trials. These systems and methods can be applied to the diagnosis and treatment of diseases such as CVD by tracking disease regression. These systems and methods can also be applied to stent imaging to assess the presence and extent of restenosis.

  Although the present invention has been described with respect to various specific embodiments where it is necessary to collect projection data in various X-ray spectra, using photon counting MECT, which measures the energy of each detected photon, It is possible to realize a scheme that uses only one collection. A threshold scheme is used that classifies photons having an energy level above a certain threshold into one group and classifies photons having an energy level below this threshold into another group. In this case, the optimum threshold between the upper and lower energy levels is selected by measurement so that the density discrimination of the substrate is optimized.

While the invention has been described in terms of various specific embodiments, those skilled in the art will recognize that the invention can be practiced with modification within the spirit and scope of the claims. Let's be done.

1 is an external view of a multiple energy computed tomography (MECT) imaging system that implements a method for detecting plaque components. FIG. It is a block schematic diagram of the MECT system shown in FIG. 2 is a flow diagram of one embodiment of a method for detecting plaque components. 2 is a flow diagram of one embodiment of a method for detecting plaque components. 2 is a flow diagram of one embodiment of a method for detecting plaque components. 2 is a flow diagram of one embodiment of a method for detecting plaque components. It is a figure showing a plurality of images. It is a figure showing another some image.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 Multi-energy computed tomography (MECT) imaging system 12 Gantry 14 X-ray source 16 X-ray beam 18 Detector array 20 Detector element 22 Patient 24 Center of rotation 26 Control mechanism 28 X-ray controller 30 Gantry motor controller 32 DAS
34 Image reconstruction device 36 Computer 38 Storage device 40 Console 44 Table motor controller 46 Table 48 Gantry opening 50 Reading device 52 Computer readable medium

Claims (10)

A multi-energy computed tomography (MECT) system,
At least one radiation source configured to emit X-rays intersecting the subject;
At least one detector configured to detect the x-ray;
A controller coupled to the detector;
A computer configured to instruct the MECT system to detect plaque components;
With
The computer further includes:
a) creating a look-up table corresponding to a plurality of substances using a plurality of simulated phantoms;
b) scanning the subject at first and second energy levels to produce projection data of the first and second energy spectra;
c) Density image of the substrate of the subject by inverse mapping the projection data of the subject from the lookup table to the density of the substrate using a substance selected from the plurality of substances as a substrate. Reconfiguring the
d) after said contrast agent is administered to the subject, among the plurality of material, different from the selected material, and selecting a substance which corresponds to the contrast medium, repeating the steps b) and c) Stages,
A MECT system configured to perform The look-up table maps different densities of selected bodies of the phantom to projection data for different energy spectra;
The computer obtains plaque components by using the look-up table;
The MECT system of claim 1, wherein the MECT system is configured to perform In order to create the lookup table, the computer
A first and second set of density distribution images of a phantom with a set of known material properties,
Simulation of the placement of the phantom within the scan range of the MECT system, and simulation of the scan of the phantom at first and second energy levels using the MECT system;
The MECT system of claim 1, wherein the MECT system is configured to be acquired by: The look-up table is formed for all known different iodine concentrations;
The computer to obtain plaque components
Instructing the MECT system to place the subject within the scan range of the MECT system;
Instructing the MECT system to scan the subject at the first and second energy levels to obtain projection data of the subject;
Determining the density of the subject from the look-up table by inverse mapping the projection data of the subject to the density of the selected substrate comprising at least one of iodine and water;
The MECT system of claim 3, wherein the MECT system is configured to perform The computer
Instructing the user to administer a contrast agent to at least one of plaque lipophiles, plaque plaque specific antigen, and plaque plaque cells;
Repeating detection of the components of the plaque;
The MECT system of claim 1, wherein the MECT system is configured to perform The contrast agent is a temperature sensitive contrast agent;
The computer
Instructing the user to administer a temperature sensitive contrast agent to the inflammatory plaque;
The MECT system of claim 1, wherein the MECT system is configured to perform 7. A MECT system according to any preceding claim, wherein the computer is configured to quantify plaque components. In order to quantify the components of the plaque, the computer
Calculating a composition distribution of the plaque;
Calculating the total plaque load,
The MECT system of claim 7, wherein the MECT system is configured to perform The computer
Instructing a display device to display at least one of a two-dimensional (2D) image and a three-dimensional (3D) image of a plaque component on a wall of an organ of the subject;
Allowing the volume of the plaque to be observed from a certain observation point in a 3D image;
The MECT system according to claim 1, wherein the MECT system is configured to perform the following. The computer
Improving the image quality of a subject having at least one of a metal stent and a valve by removing beam hardening artifacts in the image;
Allowing the restenosis within at least one of the metallic stents to be visualized by repeating the process of detecting plaque components;
The MECT system according to claim 1, wherein the MECT system is configured to perform the following.

Images